Reducing Substance Use-Related Harms: A Simulation-Optimization Framework for the Design and Evaluation of Harm Reduction Vending Machines

Reyhaneh Zafarnejad, Paul M. Griffin, Aleksandra E. Zgierska and Alice Zhang
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Reyhaneh Zafarnejad: Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
Paul M. Griffin: The Harold and Inge Marcus Department of Industrial and Manufacturing Engineering; Consortium of Substance Use and Addiction, Social Science Research Institute; The Pennsylvania State University, University Park, PA, USA
Aleksandra E. Zgierska: Penn State College of Medicine, Departments of Family & Community Medicine, Public Health Sciences, and Anesthesiology and Perioperative Medicine, Hershey, PA, USA
Alice Zhang: Penn State College of Medicine, Department of Family & Community Medicine, Hershey, PA, USA

Medical Decision Making, 2025, vol. 45, issue 8, 1052-1069

Abstract: Introduction This study introduces a simulation-optimization framework designed to optimize the services of opioid-focused harm reduction vending machines (HRVMs). Given the rising rates of overdose deaths and increased potential for infectious diseases among persons who inject drugs (PWID), HRVMs can become an important harm reduction (HR) strategy by providing essential supplies that mitigate health risks. Methods We developed and validated an agent-based simulation–optimization framework to model the impact of HRVM-item allocation on the burden of opioid-related harms, accounting for demand dynamics, item restocking, and regional characteristics. The model evaluated health outcomes—cases of HIV, HCV, and fatal and nonfatal overdose—using disability-adjusted life-years (DALYs). Scenario-based analyses were conducted for different HRVM configurations, considering current legal limits on safer-injection supplies, fentanyl’s growing role as a drug of choice, and potential future policy changes. Results The base scenario estimated optimal HRVM capacity allocation at approximately 48.5% fentanyl test strips (FTS), 26.2% naloxone, and 25.3% safer injection kits. However, sensitivity analyses showed significant variations based on fentanyl prevalence and willingness to use FTS. In scenarios of intentional fentanyl use with high FTS utilization, allocation favored FTS, while scenarios with low FTS utilization prioritized naloxone and injection kits. Adding addiction treatment referral services to HRVMs further reduced DALYs and societal costs, primarily by preventing fatal overdoses. Safer injection kits consistently reduced blood-borne infections compared with scenarios without these kits. Conclusions The framework could aid in HRVMãrelated service planning and evaluation, highlighting the importance of strategic inventory management and linkages to addiction care for enhanced health outcomes. HRVMs show potential as scalable, cost-effective HR interventions, warranting further research on their impact on service accessibility and health outcomes. Highlights A novel simulation-optimization framework for designing and evaluating harm reduction vending machines (HRVMs) is presented. Optimal baseline allocation for products in the HRVMs included fentanyl test strips (48.5%), naloxone (26.2%), and safer injection kits (25.3%). Sensitivity analysis indicated optimal allocations vary substantially by local fentanyl prevalence and by individual harm reduction behaviors surrounding the use of fentanyl test strips. HRVM implementation reduces societal costs and disability-adjusted life-years associated with substance use–related harms.

Keywords: harm reduction interventions; substance use disorder; overdose prevention; simulation-optimization; economic evaluation (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:45:y:2025:i:8:p:1052-1069

DOI: 10.1177/0272989X251367719

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